Detectiverse: Advancing Supply Chain Efficiency with AI-Enhanced Screw Counting

Year : 2024 | Volume :11 | Issue : 01 | Page : 21-26
By

Aaron Sam A. S.,

Shanmuga Pradeepan R.,

Sobana R. S.,

S. Rathnamala,

A. Karthick Kumar,

  1. Student Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar Tamil Nadu India
  2. Student Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar Tamil Nadu India
  3. Student Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar Tamil Nadu India
  4. Associate Professor Department of computer science and engineering, Sethu Institute of Technology, Virudhunagar Tamil Nadu India
  5. Assistant Professor Department of Computer Science and Engineering, Sethu Institute of Technology, Virudhunagar Tamil Nadu India

Abstract

Accurate screw counting is essential in the manufacturing sector to ensure efficient inventory management and maintain quality control standards. The current manual counting method is prone to errors and lacks the ability to identify the source of missing screws. To address this challenge, we propose implementing an automated screw counting system at Indo Metal Tech in Ambattur, Chennai. This system would utilize advanced image processing and machine learning algorithms to detect and tally screws within trays captured by cameras. The algorithm incorporates user-triggered actions, real-time feedback, and user verification to ensure accuracy and adaptability to changing manufacturing conditions. This innovative solution not only improves efficiency and accuracy but also facilitates collaborative manufacturing practices. The system, equipped with a camera and sophisticated image processing algorithms, effectively and precisely handles these challenges. Additionally, the solution’s versatility is enhanced by QR codes for collaborative counting and the option for continuous counting between trays. This reduces human error rates and establishes a framework for enhanced traceability and quality control in the manufacturing process. Overall, this proposed solution not only meets the immediate need for precise screw tallying but also lays the groundwork for an improved, simplified, and collaborative manufacturing process.

Keywords: Computer vision, image processing, object detection, pre-trained models, screw counting

[This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]

How to cite this article: Aaron Sam A. S., Shanmuga Pradeepan R., Sobana R. S., S. Rathnamala, A. Karthick Kumar. Detectiverse: Advancing Supply Chain Efficiency with AI-Enhanced Screw Counting. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(01):21-26.
How to cite this URL: Aaron Sam A. S., Shanmuga Pradeepan R., Sobana R. S., S. Rathnamala, A. Karthick Kumar. Detectiverse: Advancing Supply Chain Efficiency with AI-Enhanced Screw Counting. Journal of Image Processing & Pattern Recognition Progress. 2024; 11(01):21-26. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=138421



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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received March 11, 2024
Accepted March 20, 2024
Published April 3, 2024